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This book constitutes the refereed proceedings of the Computer Games Workshop, CGW 2014, held in conjunction with the 21st European Conference on Artificial Intelligence, ECAI 2014, Prague, Czech Republic, in August 2014. The 11 revised full papers presented were carefully reviewed and selected from 20 submissions. The papers address all aspects of artificial intelligence and computer game playing. They discuss topics such as general game playing, video game playing, and cover 11 abstract games: 7 Wonders, Amazons, AtariGo, Ataxx, Breakthrough, Chinese Dark Chess, Connect6, NoGo, Pentalath, Othello, and Catch the Lion.
Kubernetes radically changes the way applications are built and deployed in the cloud. Since its introduction in 2014, this container orchestrator has become one of the largest and most popular open source projects in the world. The updated edition of this practical book shows developers and ops personnel how Kubernetes and container technology can help you achieve new levels of velocity, agility, reliability, and efficiency. Kelsey Hightower, Brendan Burns, and Joe Beda—who’ve worked on Kubernetes at Google and beyond—explain how this system fits into the lifecycle of a distributed application. You’ll learn how to use tools and APIs to automate scalable distributed systems, whether it’s for online services, machine learning applications, or a cluster of Raspberry Pi computers. Create a simple cluster to learn how Kubernetes works Dive into the details of deploying an application using Kubernetes Learn specialized objects in Kubernetes, such as DaemonSets, jobs, ConfigMaps, and secrets Explore deployments that tie together the lifecycle of a complete application Get practical examples of how to develop and deploy real-world applications in Kubernetes
In ancient games such as chess or go, the most brilliant players can improve by studying the strategies produced by a machine. Robotic systems practice their own movements. In arcade games, agents capable of learning reach superhuman levels within a few hours. How do these spectacular reinforcement learning algorithms work? With easy-to-understand explanations and clear examples in Java and Greenfoot, you can acquire the principles of reinforcement learning and apply them in your own intelligent agents. Greenfoot (M.Kölling, King's College London) and the hamster model (D. Bohles, University of Oldenburg) are simple but also powerful didactic tools that were developed to convey basic programming concepts. The result is an accessible introduction into machine learning that concentrates on reinforcement learning. Taking the reader through the steps of developing intelligent agents, from the very basics to advanced aspects, touching on a variety of machine learning algorithms along the way, one is allowed to play along, experiment, and add their own ideas and experiments.
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
These lecture notes were prepared for use in the 2023 ASU research-oriented course on Reinforcement Learning (RL) that I have offered in each of the last five years. Their purpose is to give an overview of the RL methodology, particularly as it relates to problems of optimal and suboptimal decision and control, as well as discrete optimization. There are two major methodological RL approaches: approximation in value space, where we approximate in some way the optimal value function, and approximation in policy space, whereby we construct a (generally suboptimal) policy by using optimization over a suitably restricted class of policies.The lecture notes focus primarily on approximation in value space, with limited coverage of approximation in policy space. However, they are structured so that they can be easily supplemented by an instructor who wishes to go into approximation in policy space in greater detail, using any of a number of available sources, including the author's 2019 RL book. While in these notes we deemphasize mathematical proofs, there is considerable related analysis, which supports our conclusions and can be found in the author's recent RL and DP books. These books also contain additional material on off-line training of neural networks, on the use of policy gradient methods for approximation in policy space, and on aggregation.
Covers the entire process of product development from idea to launch without missing a step!